Streamlining Federated Learning Workflows for Secure Real Time Model Updates in Edge Computing
Introduction Edge computing has moved from a niche research area to the backbone of modern IoT ecosystems, autonomous systems, and latency‑critical applications. At the same time, privacy‑preserving machine learning techniques—most notably Federated Learning (FL)—have become the de‑facto approach for training models on distributed data without ever moving raw data to a central server. When these two trends intersect, a compelling question arises: How can we streamline federated learning workflows to deliver secure, real‑time model updates to edge devices? ...